In this work, we explore UAV-assisted reconfigurable intelligent surface (RIS) technology to enhance downlink communications in wireless networks. By integrating RIS on both UAVs and ground infrastructure, we aim to boost network coverage, fairness, and resilience against challenges such as UAV jitter. To maximize the minimum achievable user rate, we formulate a joint optimization problem involving beamforming, phase shifts, and UAV trajectory. To address this problem, we propose an adaptive soft actor-critic (ASAC) framework. In this approach, agents are built using adaptive sparse transformers with attentive feature refinement (ASTAFER), enabling dynamic feature processing that adapts to real-time network conditions. The ASAC model learns optimal solutions to the coupled subproblems in real time, delivering an end-to-end solution without relying on iterative or relaxation-based methods. Simulation results demonstrate that our ASAC-based approach achieves better performance compared to the conventional SAC. This makes it a robust, adaptable solution for real-time, fair, and efficient downlink communication in UAV-RIS networks.